Financial & Credit Risks

How to Prioritize Accounts Receivable Collections

Reduce Risk and Save Resources With Data-Driven Collections Prioritization

Debt collection is an unfortunate reality for most businesses. When customers are ultimately unable to pay for their purchase, the impact of those lost dollars can be significant. For small businesses especially, failure to collect on outstanding accounts receivables can have dire consequences.

Companies may employ a variety of methods to collect on past due invoices, such as phone calls, emails, letters, and site visits. These can be time-consuming and costly. Regardless of the method used, when it comes to business debt collections, efficiency is the name of the game. The sooner companies can receive payment on goods and services rendered, the better it is for their bottom line.

When it comes to staying ahead of bad debt, what strategies should you apply to ensure that outstanding debt is recovered in the most time- and cost-effective way? This article discusses some of the most common business debt collections best practices, how they work, and why they might not work for you.

Accounts Receivable Collections Best Practices

For most companies, it’s the accounts receivable department’s responsibility to manage debt collection. However, in some instances, customers may dispute an invoice and refuse to pay if they’re dissatisfied with the products or services they received. Billing errors and pricing issues, such as promotions and discounts not applied, are also common reasons for disputes. When this happens, other departments, such as sales and customer service, may step in to assess the problems and determine the best course of action. Often, the issue is resolved before it’s ever deemed uncollectible.

However, if a dispute cannot be resolved, the delinquent account might land in collections. Typically, the process goes as follows:

  1. Before any action is taken, it’s best to confirm that the customer was, in fact, issued an invoice.
  2. Next, the customer is contacted via automated phone call or email and reminded that their account is past due. They are asked to pay immediately, or risk incurring late payment penalties or interest.
  3. If the customer does not respond within 24-72 hours, a staff member may follow up by phone.
  4. At the same time, a letter is sent by mail.

This process may be repeated several times, depending on the company’s grace period. The average collection period for accounts receivable is 30 days, and payments are considered severely delinquent when they’re more than 90 days past due. When the payment hits the 120-day mark or is deemed uncollectable, the account may be sent to a 3rd party collections agency (or debt collector).

When to Outsource to a Collections Agency

Collections agencies can be a costly option, and for that reason, they’re typically used as a last resort. Debt collectors only get paid when they recover an outstanding debt, but when they do, it comes at a cost of anywhere from 25%-45% of the total amount owed. Despite this, these agencies do provide a necessary service for companies that can’t afford to keep tying up resources chasing after customers who are obviously not able to pay their debt.

So how can companies get paid for outstanding debts without putting themselves at risk or losing money? Data-driven, risk-based collections prioritization may be the solution.

How to Prioritize Business Debt Collections with Data

Most companies prioritize collections based on which debtors owe the most, and which ones are the most delinquent. For example, if one customer owes $10,000 and is 6 months past due, they would be prioritized over another customer who owes $5,000 and is 3 months past due. The idea behind this approach is that the more money you can recover all at once, the sooner you can use that working capital. While in theory, this approach sounds reasonable, it comes with a certain level of risk.

One reason why this method is risky is because the longer an account remains delinquent, the less likely the account holder is to pay their debt. More specifically, an account that is 90 days past due has a 69.6% chance of being paid. After six months, the probability rate drops to 52.1%, and after one year of delinquency, the chance of collecting payment falls to 22.8%, according to one collections agency.

Additionally, prioritizing collections without factoring in vital customer data, such as business credit scores, trade payment history, and financial statements, can be dangerous. A data-driven, predictive collections prioritization model informs collections staff which accounts are the most or least likely to pay; and it can save your company from wasting precious time and resources trying to collect money you may never receive.

New Accounts Receivable Collections Prioritization Techniques

To illustrate the difference between a traditional collections prioritization method and a risked-based method, imagine your team is focused on recovering the above-mentioned $10,000 debt, while unbeknownst to them, the account holder for the $5,000 debt is at high risk of going out of business. If the team was able to receive alerts notifying them when customers’ commercial credit scores suddenly change and signaling when one of them is at risk for bankruptcy or insolvency, they could quickly pivot their efforts. In this particular case, it would make more sense to move the $5,000 account to the top of the list and attempt to collect before the business goes into further financial distress.

There are companies that provide personalized analytics solutions specifically for collections prioritization. Dun & Bradstreet’s Collection Prioritization Index helps companies predict recovery rates for the most severely delinquent accounts to prioritize collections using a variety of data inputs. An analytics model like this analyzes the stability of your customers’ businesses, past payment histories, credit scores, and other attributes to predict how likely they are to pay and when.

Regardless of the types of collections methods you use, when it comes to prioritization, taking a data-driven, risk-based approach will help you better predict write-off levels which in turn, will save your company precious time, money, and resources.

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